Communication Distribution Model for Organizational Workflow Optimization
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Solution Overview
Problem
Current employee management decisions in organizations rely on subjective methods, leading to poor organizational health, confusion, and decreased productivity, as they lack objective measures for communication and collaboration within the organization.
Innovation Solution
A system and method that utilize sensing and modeling of social network data within organizations, integrating AI, machine learning, and behavioral science to objectively quantify communication and collaboration by capturing and analyzing digital and physical communication data, providing insights into communication patterns and workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective methods (tribal knowledge, industry best practices, organizational charts, employee surveys) are used for employee management decisions, then decision-making simplicity is maintained, but measurement precision and reliability of organizational health assessment deteriorate
Solution Approach 1:
The patent combines multiple data sources (digital communication metadata from email/chat/phone systems and physical sensor data from wearable devices) into a unified communication distribution model. This merging of digital and physical data streams enables objective measurement of organizational communication patterns while maintaining manageable system complexity through integrated processing.
Solution Approach 2:
The patent introduces a communication distribution model as an intermediary layer that transforms raw communication metadata and sensor data into meaningful organizational insights. This model acts as a mediator between data collection and decision-making, providing objective measurements without requiring direct complex analysis of all raw data.
2Measurement precision
If comprehensive digital and physical communication data is captured and analyzed, then measurement precision of communication patterns improves, but loss of time for data processing and analysis increases
Solution Approach 1:
The patent extracts only the necessary metadata from comprehensive communication data (sender, recipient, timestamp, communication type) rather than processing entire message contents. This extraction approach maintains measurement precision for communication pattern analysis while significantly reducing data processing time and computational resources.
Solution Approach 2:
The patent implements partial action by focusing on key communication metrics (attention time, interaction frequency) rather than analyzing every aspect of organizational communication. This selective measurement approach provides sufficient objective quantification for management decisions without the time cost of comprehensive analysis.
3Measurement precision
If detailed communication metadata and sensor data are collected from all participants, then measurement precision of collaboration patterns improves, but loss of information privacy and participant anonymity increases
Solution Approach 1:
The patent creates aggregated communication distribution models that copy and represent organizational communication patterns without revealing individual participant identities. The model uses anonymized data to generate objective measurements of communication flows, allowing precision in pattern recognition while preserving participant privacy through systematic obfuscation of personal identifiers.
Data Source
AI summary
A method and system for transforming communication metadata and sensor data into an objective measure of the participant communication distribution of an organization is disclosed. Embodiments of the present disclosure enable the capturing communication data from phone, email, or other virtual means of communication to develop a complete objective model of a human network. Metadata is extracted from the captured communication data and the extracted communication metadata is analyzed by (i) calculating time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise adjacency matrix based on the attention time each participant spent communicating with each other participant. Based on the pairwise adjacency matrix, a visualization may be created illustrating the communication distribution of the participants throughout the organization.